It is quite strenuous to have manual surveillance around the clock in sensitive areas. Even with video cameras fitted in most of the places where security is a concern, the volume of the data generated by video is so enormous that it might demand prohibitively large data storage requirements.
The word surveillance may be applied to observation from a distance by means of electronic equipment such as CCTV cameras. Surveillance is useful to private and government security agencies to maintain social control, recognize and monitor threats, and prevent/investigate, trespassing and criminal activities. The video surveillance is literally used everywhere including in sensitive areas and like airports, nuclear power plants, laboratories, banks. It is also used at traffic signals, streets, doors etc. Organizations responsible for conducting such surveillance typically deploy a plurality of sensors (e.g., Closed Circuit Television Video (CCTV) and infrared cameras, radars, etc.) to ensure security and wide-area awareness.
In the current state of the art, the classification of the moving object during the video surveillance into predefined classes like human, animal (cattle) and vehicle is done by different processes.
The techniques used in the prior arts like temporal differencing, background subtraction, optical flow, use of motion detection mask, periodic motion and image correlation matching for the classification of the moving object in to predetermined classes are computationally expensive and require more memory space for storing and analyzing sequence of frames in the video containing object of interest.
As compared to the prior arts, the proposed the system and method for classification of moving objects in the video surveillance into predefined classes, using a computationally inexpensive method which is more economic, using simple logic for discriminating objects during video surveillance, utilizing less memory storage space avoiding the storage of frames and utilizing less memory storage space while computing discriminatory feature.